| Oct 2021 - Present |
Sr AI Engineer at Jumio Corporation, Italy / Austria
Research and Development for Bank Document and ID Verification from Photos |
| June 2020 - Oct 2021 |
Post-doctoral Researcher at Multimedia Group Unitn, Italy
Research in Graph Neural Networks for Computer Vision and Unsupervised Learning |
| May 2019 - Nov 2019 |
Deep Learning Intern at IBM Research, Zurich
Graph Neural Networks for record linkage and data modelling, Demo video. |
| June 2018 - Aug 2018 |
Machine Learning Intern at IBM Benelux, Amsterdam
Active learning and adaptive text classification algorithms. |
| Oct 2017 - Dec 2017 |
Deep Learning Intern at Pi School, Rome
Ransomware detection through analysis of user logs and RNN models, Demo video. |
| Jan 2016 – May 2016 |
Data Fusion intern at Purdue University, USA
Data fusion from noisy sources of information for object lifespan and ground truth inference. |
| Feb 2015 - Sep 2015 |
Python Software Engineer at iTechArt Group, Belarus
Dvelopment endpoints for RESTful web services. |
| Aug 2014 - Feb 2015 |
Embedded Software Engineer at Promwad Electronics, Belarus
Embedded software for one board Linux computers and microcontrollers. |
Hello! I am a Senior AI Engineer and Tech Lead at Jumio Corporation, where I’ve been driving innovation since October 2021. I lead Document Classification Team, focusing on ID verification for top clients like Airbnb, Uber, and TikTok. My work involves designing and productionalizing multimodal solutions with text and vision transformers, defining the AI roadmap, mentoring engineers, and coordinating cross-team efforts.
I was a Post-doctoral researcher in Multimedia Group at the University of Trento where my work spans over Graph Neural Networks for Computer Vision, Domain Adaptation, and Self-supervised Learning, in particular, when little or no supervision is available.
I hold a PhD (Cum laude) in Computer Science from the University of Trento in 2020, where I worked on natural language, cost-aware active learning, crowdsourcing and hybrid crowd-machine learning algorithms for low-risk classification decisions.
In my work, I use a variety of Machine Learning and Deep Learning approaches, such as NLP, Transformers, LLMs, CNN, and Graph Neural Networks. For obtaining high-quality training data from (possibly) noisy sources, I implement state-of-the-art crowdsourcing and data fusion algorithms.